Search Results for author: Kotagiri Ramamohanarao

Found 11 papers, 6 papers with code

Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective

no code implementations22 Oct 2022 Caesar Wu, Kotagiri Ramamohanarao, Rui Zhang, Pascal Bouvry

Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex.

Decision Making

Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

1 code implementation23 Sep 2020 Jiabo He, Sarah Erfani, Sudanthi Wijewickrema, Stephen O'Leary, Kotagiri Ramamohanarao

Semantic segmentation is one of the key problems in the field of computer vision, as it enables computer image understanding.

Medical Image Segmentation Semantic Segmentation

Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

1 code implementation1 Sep 2020 Shreshth Tuli, Shashikant Ilager, Kotagiri Ramamohanarao, Rajkumar Buyya

The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources.


Generative Image Inpainting with Submanifold Alignment

no code implementations1 Aug 2019 Ang Li, Jianzhong Qi, Rui Zhang, Xingjun Ma, Kotagiri Ramamohanarao

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal.

Image Inpainting Image Restoration

A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations

no code implementations24 Aug 2018 Jiayuan He, Jianzhong Qi, Kotagiri Ramamohanarao

We propose two trip recommendation algorithms based on our context-aware POI embedding.

Learning with Bounded Instance- and Label-dependent Label Noise

no code implementations ICML 2020 Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, DaCheng Tao

Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN.

Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction

3 code implementations9 Feb 2015 Yong Luo, DaCheng Tao, Yonggang Wen, Kotagiri Ramamohanarao, Chao Xu

As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.

Dimensionality Reduction MULTI-VIEW LEARNING

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